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|c 5.00 USD
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|z 9781475599022
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Chan-Lau, Jorge.
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|a Lasso Regressions and Forecasting Models in Applied Stress Testing /
|c Jorge Chan-Lau.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2017.
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| 300 |
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|a 1 online resource (34 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.
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|a Mode of access: Internet
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|a IMF Working Papers; Working Paper ;
|v No. 2017/108
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2017/108/001.2017.issue-108-en.xml
|z IMF e-Library
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